Method and system for searching and verifying magnitude change events in video surveillance

ABSTRACT

A method for detecting events in a video sequence includes providing a video sequence, sampling the video sequence at regular intervals to form a series of snapshots of the sequence, measuring a similarity of each snapshot, measuring a similarity change between successive pairs of snapshots, wherein if a similarity change magnitude is greater than a predetermined threshold, a change event has been detected, verifying the change event to exclude a false positive, and completing the processing of the snapshot incorporating the verified change event.

CROSS REFERENCE TO RELATED UNITED STATES APPLICATIONS

This application claims priority from “Efficient search of events forvideo surveillance”, U.S. Provisional Application No. 60/540,102 of ImadZoghlami, et al., filed Jan. 27, 2004, the contents of which areincorporated herein by reference.

TECHNICAL FIELD

The invention is directed to the detection and characterization ofevents in a long video sequence.

DISCUSSION OF THE RELATED ART

In various applications of machine vision it is important to be able todetect changes and events by interpreting a temporal sequence of digitalimages. The resulting sequences of imaged changes can then be madeavailable for further scene analysis evaluation by either a person or anintelligent system. A practical system for detecting events must be ableto distinguish object motions from other dynamic processes.

Examples of applications of such methods are found in video-basedsystems for monitoring and control functions, for example in productionengineering or in road traffic control and instrumentation (intelligenttraffic light control). The determination of spatial structures and theanalysis of spatial movements is of the highest significance forapplications in robotics, as well as for aims in autonomous navigation.For the purpose of supporting vehicle drivers, there is a need forsystems which are capable, with the aid of one or more video cameras andof the vehicle speed determined by the tachometer, and with the aid ofother data such as measured distance data, for example, of detectingmoving objects in the environment of the vehicle, the spatial structureof the vehicle environment and the intrinsic movement of the vehicle inthe environment, and of tracking the movement of detected objects.Finally, in communication technology the reduction of image data forpurposes of transmission and storage of image data is steadily gainingin significance. Precisely in the case of coding temporal imagesequences, analysis of movements delivers the key to a decisivereduction in datasets or data rates.

Current research has focused on extraction of motion information, andusing the motion information for low level applications such asdetecting scene changes.

There still is a need to extract features for higher level applications.For example, there is a need to extract features that are indicative ofthe nature of the activity and unusual events in a video sequence. Avideo or animation sequence can be perceived as being a slow sequence, afast paced sequence, an action sequence, and so forth.

Examples of high activity include scenes such as goal scoring in asoccer match, scoring in a basketball game, a high speed car chase. Onthe other hand, scenes such as news reader shot, an interview scene, ora still shot are perceived as low action shots. A still shot is onewhere there is little change in the activity frame-to-frame. Videocontent in general spans the gamut from high to low activity. It wouldalso be useful to be able to identify unusual events in a video relatedto observed activities. The unusual event could be a sudden increase ordecrease in activity, or other temporal variations in activity dependingon the application.

SUMMARY OF THE INVENTION

Exemplary embodiments of the invention as described herein generallyinclude methods and systems for efficiently searching for events in avideo surveillance sequence. Disclosed herein are methods for detectingobject appearance/disappearance in the presence of illumination changes,and in the presence of occlusion either before or after disappearance,and occlusion before or after appearance of an object. The videosurveillance sequences can be either indoor or outdoor sequences.

In one aspect of the invention, there is provided a method for detectingevents in a video sequence including the steps of providing a videosequence, sampling the video sequence at regular intervals to form aseries of snapshots of the sequence, measuring a similarity of eachsnapshot, measuring a similarity change between successive pairs ofsnapshots, wherein if a similarity change magnitude is greater than apredetermined threshold, a change event has been detected, verifying thechange event to exclude a false positive, and completing the processingof the snapshot incorporating the verified change event. In a furtheraspect of the invention, the sampling interval is from a few seconds toa few minutes. In a further aspect of the invention, the methodcomprises defining one or more windows-of-interest in each snapshot, andmeasuring the similarity in each window-of-interest in each snapshot. Ina further aspect of the invention, the similarity measure for awindow-of-interest in a snapshot is defined as

${{S_{w}^{0}(t)} = {\frac{1}{W}\sqrt{\sum\limits_{x \in W}{\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)}}}},$where x_(t) represents the pixel intensity for a pixel in awindow-of-interest W of snapshot t, and x _(t) is a spatial intensityaverage in the window for the snapshot. In a further aspect of theinvention, the similarity measure is normalized to the spatial intensityscale of the window-of-interest according to the formula

${S_{w}(t)} = {\frac{1}{{W}*{\overset{\_}{x}}_{t}}{\sqrt{\sum\limits_{x \in W}{\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)*\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)}}.}}$In a further aspect of the invention, the change in the similaritymeasure is determined from the time derivative of the similaritymeasure. In a further aspect of the invention, a false positive includesan occlusion. In a further aspect of the invention, the method compriseseliminating an occlusion by weighting a time derivative of thesimilarity measure according to the definition

${f_{w}(t)} = {{g(t)}*{{\overset{.}{S}}_{w}(t)}\mspace{14mu}{wherein}}$${{g(t)} = {h\left( {\min\limits_{{i \in {\lbrack{n_{1},n_{2}}\rbrack}},{j \in {\lbrack{n_{1},n_{2}}\rbrack}}}{{similarity}\left( {w_{t - i},w_{t + j}} \right)}} \right)}},{wherein}$${{{similarity}\left( {w_{i},w_{j}} \right)} = {\frac{1}{n}{\sum\limits_{k = 1}^{n}{{{{hist}_{i}\lbrack k\rbrack} - {{hist}_{j}\lbrack k\rbrack}}}}}},$and wherein {dot over (S)}_(w)(t) is the similarity measure timederivative, w_(i), w_(j) are corresponding windows-of-interest in a pairof successive snapshots, [n₁,n₂] is the duration neighborhood about thesnapshot incorporating the occlusion over which similarity is beingsought, h is a positive increasing function with h(1)=1, and hist is ahistogram of spatial intensity values in the window-of-interest. In afurther aspect of the invention, h(x)∝x². In a further aspect of theinvention, a false positive includes a change of illumination. In afurther aspect of the invention, the predetermined threshold is based onan analysis of the fluctuations in the similarity change betweensuccessive pairs of snapshots. In a further aspect of the invention, thethreshold is more than three standard deviations greater than the meanfluctuation magnitude of the similarity change between successive pairsof snapshots.

In another aspect of the invention, there is provided a program storagedevice readable by a computer, tangibly embodying a program ofinstructions executable by the computer to perform the method steps fordetecting events in a video sequence

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 presents an overview of an event detection method according toone embodiment of the invention.

FIG. 2 presents a flow chart of an event detection method according toone embodiment of the invention.

FIG. 3 presents a result of applying a change detection method accordingto one embodiment of the invention to a video sequence of a parking lot

FIG. 4 depicts a graph of the variance time derivative for the videosequence of FIG. 3, along with the threshold.

FIG. 5 depicts the result of using an interval similarity weighting todetect occlusion, according to one embodiment of the invention.

FIG. 6 presents an example of the background modeling results, accordingto one embodiment of the invention.

FIG. 7 presents a schematic block diagram of a system that can implementthe methods of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Exemplary embodiments of the invention as described herein generallyinclude systems and methods for detecting events in a video surveillancerecording. In the interest of clarity, not all features of an actualimplementation which are well known to those of skill in the art aredescribed in detail herein.

In order to quickly detect and characterize an event in a long videosurveillance recording, one is frequently seeking to detect significantchanges in the video images. For example, one purpose of a videosurveillance in a parking lot would be to monitor individual parkingspaces, to see when an empty space is occupied by a vehicle, or when anoccupied space is vacated. The appearance/disappearance of a vehiclerepresents a significant change in the image recorded in the videosurveillance data, and the time scale over which such an event occurs isrelatively short when compared to the duration of the recording, i.e.,on the order of a minute in a recording that is on the order of one ormore hours in duration.

An overview of an event detection method according to one embodiment ofthe invention is presented in FIG. 1. Starting with an original videosurveillance tape 10 of some duration, the tape is sampled at discreteintervals to form a set of snapshots of the surveillance tape. Theoriginal video sequence can be either analog or digital, however, theresulting snapshots are digital images. The sampling interval is chosensufficiently far apart so that significant events, rather than smallchanges, are detectable. Upon application 11 of the change detectionmethods according to an embodiment of the present invention, a subset 12of the original video that contains change events of interest isselected for further analysis. These selected changes are analyzed 13using change verification methods according to an embodiment of thepresent invention, after which one or more events 14 are selected forfine processing to extract information 15. Methods for fine processingof images, such as background modeling, are well known in the art, andcan be applied to the selected event to confirm detection 16 of anevent.

Referring now to the flow chart of FIG. 2, according to an embodiment ofthe invention, a method for detecting change in a video sequenceincludes the steps of providing a video sequence for analysis 200,sampling the sequence 201, determining a measure of similarity 202,detecting change 203, and verifying that a change occurred 204. Once thechange has been verified, the image processing can be completed 205according to other methods as needed and as are known in the art. Giventhe video sequence, a first step 201 of the method is a regularsampling, to create a series of digital images to form snapshots of thevideo sequence. Within the snapshots are one or more windows-of-interest(WOIs) wherein attention is focused. To help with the detection ofsignificant changes, the sampling removes all smooth changes in theWOIs, like the progressive appearance or disappearance of an object.According to one embodiment of the invention, the sampling interval canbe from a few seconds to a few minutes.

A useful measure of similarity in accordance with an embodiment of theinvention is determined at step 202 from the image intensity variancewithin a WOI of a particular snapshot:

${{S_{w}^{0}(t)} = {\frac{1}{W}\sqrt{\sum\limits_{x \in W}{\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)}}}},$where x_(t) represents the pixel intensity for a pixel in the window Wat time (i.e. snapshot) t, and x _(t) is the spatial intensity averagein the window for the snapshot. This variance is invariant to anyintensity shift, and can be used for handling both static and movingobjects. Note that moving objects are considered only within a WOI. Amore robust similarity measure, according to another embodiment of theinvention, is a variance normalized to the spatial intensity scale:

${S_{w}(t)} = {\frac{1}{{W}*{\overset{\_}{x}}_{t}}{\sqrt{\sum\limits_{x \in W}{\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)*\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)}}.}}$This variance is invariant to any affine intensity scale changes.

Changes are detected across time at step 203 by looking for largechanges in the magnitude of the similarity measure between imagesadjacent in time. More precisely, the time derivative of the similaritymeasure is computed:

${{{\overset{.}{S}}_{w}(t)} = \frac{\partial{S_{w}(t)}}{\partial t}},$and large values of this similarity measure time derivative areindicative of an event occurrence between successive snapshots.According to one embodiment of the invention, a threshold is defined sothat a derivative magnitude greater than the threshold signifies apotential event of interest, and the corresponding snapshots areselected for further analysis. A suitable threshold can be determinedfrom an analysis of the fluctuations of the similarity measure timederivative. According to one embodiment of the invention, the thresholdis defined so that a fluctuation whose magnitude is more than threestandard deviations greater than the mean fluctuation magnitude isindicative of a change event of interest. This definition is exemplaryand other definitions of a fluctuation threshold are within the scope ofthe invention.

A result of applying these change detection methods to a video sequenceof a parking lot is depicted in FIG. 3. A 72 minute video sequence of aparking lot, with some illumination changes, was sampled at 30 secondintervals. The event sought is whether a vehicle parks in a particularspace. The left image of FIG. 3 is a snapshot of the beginning of thevideo sequence, the middle image is the snapshot just before the event,and the right image is the snapshot just after the event. The boxoutlined in the lower center of the left image is the WOI. As can beseen, this WOI is an empty space, and is still empty in the middleimage. The parking space is occupied by a car in the right image. FIG. 4depicts a graph of the variance time derivative (the fluctuating linewith a peak) for this series of snapshots, along with the threshold (thestraight line). The variance time derivative exhibits a spike, whosemagnitude is well above the threshold, that can be correlated to theappearance of the car in the parking space. The computations involvedcan be completed within three seconds.

At step 204, the change is verified to exclude false positives. Onesource of false positives resulting from the change detection methods ofthe present invention is occlusion, that is, the sudden blocking of thevideo image due to, for example, a blockage in front of the camera lens.This could result from a person walking through the field of view of thevideo camera, or even a bird flying in front of the camera lens. Unlikean illumination change, occlusion is likely to change the overallintensity profile of the WOI. To assist in the detection of a change dueto occlusion, the window of interest should be similar before and afterthe occlusion. According to an embodiment of the invention, an intervalsimilarity is computed at each time t and is weighted according to thedefinition

${f_{w}(t)} = {{g(t)}*{{\overset{.}{S}}_{w}(t)}\mspace{14mu}{where}}$${{g(t)} = {h\left( {\min\limits_{{i \in {\lbrack{n_{1},n_{2}}\rbrack}},{j \in {\lbrack{n_{1},n_{2}}\rbrack}}}{{similarity}\left( {w_{t - i},w_{t + j}} \right)}} \right)}},{and}$${{similarity}\left( {w_{i},w_{j}} \right)} = {\frac{1}{n}{\sum\limits_{k = 1}^{n}{{{{{hist}_{i}\lbrack k\rbrack} - {{hist}_{j}\lbrack k\rbrack}}}.}}}$Here, w_(i), w_(j) are corresponding WOIs in a pair of successivesnapshots, [n₁,n₂] is the duration neighborhood about the snapshotincorporating the occlusion over which similarity is being sought, h isa positive increasing function with h(1)=1, and hist is a histogram ofspatial intensities in the WOI, where the similarity is computed using ahistogram comparison. By duration neighborhood is meant the set ofsnapshots preceding the occlusion and subsequent to the occlusion. Forexample, if an occlusion occurred in the 20^(th) snapshot (i.e., t=20 inthe equation for g(t), above), [n₁,n₂] could indicate the 17^(th)snapshot through the 22^(rd) snapshot (i.e. n₁=3, n₂=2). Note that anyfunction satisfying the criteria for h can be used, such as anexponential function or a power function. According to one embodiment ofthe invention, h(x)∝x². If the neighborhood used is small, then the timebetween the compared windows can be made small, on the order of a fewminutes. In that case, the change in illumination should be small, andshould not have any significant effect on the detection of occlusion.The weighting function thus defined is a penalty function, in that anevent due to an occlusion is penalized by having the magnitude of thesimilarity measure time derivative reduced.

FIG. 5 depicts the result of using an interval similarity weighting todetect occlusion. In the top left of FIG. 5 is the first image of asequence. The top center image of the figure shows the appearance eventimage (the appearance of a chair), while the top right image of thefigure shows the occlusion event image. The WOI is indicated by the boxoutlined in the lower left of the image. The bottom left of FIG. 5 is agraph of {dot over (S)}_(w)(t), where event 1 is the appearance of thechair at time 41 and event 2 at time 55 is the occlusion. Note that thespike in the similarity derivative for the occlusion is much greater inmagnitude than that of event 1. The bottom right of FIG. 5 is a graph off_(w)(t)=g(t)*{dot over (S)}_(w)(t), indicating how the weightingfunction has magnified the magnitude of the appearance event spike,while reducing that of the occlusion spike. As shown in the figure, theweighted similarity derivative function ƒremoves the occlusion byincreasing the spike corresponding to the real appearance at time 41 onthe horizontal axis while reducing that of the occlusion.

Another source of false positives is a change of illumination. To verifyif a detected event is due to a change of illumination, a method such asthat disclosed in United States Patent Application No. 2003/0228058,incorporated herein by reference in its entirety, can be used.

Finally, after the event selection, any processing method as is known inthe art can be applied at step 205 to complete the processing of theimage sequences. For example, a background modeling technique can beused to remove the background and isolate the event of interest. FIG. 6presents an example of background modeling results, according to anembodiment of the invention. The left image of the figure depicts aparking lot at the beginning of a video sequence, with the WOI denotedby the box outline. The middle image shows the WOI before a changeevent, with background modeling applied to the image, while the rightimage shows the WOI after a change event, with background modelingapplied to the image. The result is that irrelevant detail is removedfrom the final image.

System Implementations

It is to be understood that the embodiments of the present invention canbe implemented in various forms of hardware, software, firmware, specialpurpose processes, or a combination thereof. In one embodiment, thepresent invention can be implemented in software as an applicationprogram tangible embodied on a computer readable program storage device.The application program can be uploaded to, and executed by, a machinecomprising any suitable architecture.

Referring now to FIG. 7, according to an embodiment of the presentinvention, a computer system 701 for implementing the present inventioncan comprise, inter alia, a central processing unit (CPU) 702, a memory703 and an input/output (I/O) interface 704. The computer system 701 isgenerally coupled through the I/O interface 704 to a display 705 andvarious input devices 706 such as a mouse and a keyboard. The supportcircuits can include circuits such as cache, power supplies, clockcircuits, and a communication bus. The memory 703 can include randomaccess memory (RAM), read only memory (ROM), disk drive, tape drive,etc., or a combinations thereof. The present invention can beimplemented as a routine 707 that is stored in memory 703 and executedby the CPU 702 to process the signal from the signal source 708. Assuch, the computer system 701 is a general purpose computer system thatbecomes a specific purpose computer system when executing the routine707 of the present invention. The computer system 701 also includes anoperating system and micro instruction code. The various processes andfunctions described herein can either be part of the micro instructioncode or part of the application program (or combination thereof) whichis executed via the operating system. In addition, various otherperipheral devices can be connected to the computer platform such as anadditional data storage device and a printing device.

It is to be further understood that since the exemplary systems andmethods described herein can be implemented in software, the actualmethod steps may differ depending upon the manner in which the presentinvention is programmed. Given the teachings herein, one of ordinaryskill in the related art will be able to contemplate these and similarimplementations or configurations of the present invention. Indeed,while the invention is susceptible to various modifications andalternative forms, specific embodiments thereof have been shown by wayof example in the drawings and are herein described in detail. It shouldbe understood, however, that the description herein of specificembodiments is not intended to limit the invention to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the invention as defined by the appended claims.

1. A method for detecting events in a video sequence, said method comprising the steps of: providing a video sequence; sampling the video sequence at regular intervals to form a series of snapshots of the sequence; measuring a similarity of each snapshot; measuring a similarity change between successive pairs of snapshots, wherein if a similarity change magnitude is greater than a predetermined threshold, a change event has been detected; verifying the change event to exclude a false positive, wherein a false positive includes an occlusion; eliminating an occlusion by weighting a time derivative of the similarity measure according to the definition ${f_{w}(t)} = {{g(t)}*{{\overset{.}{S}}_{w}(t)}\mspace{14mu}{wherein}}$ ${{g(t)} = {h\left( {\min\limits_{{i \in {\lbrack{n_{1},n_{2}}\rbrack}},{j \in {\lbrack{n_{1},n_{2}}\rbrack}}}{{similarity}\left( {w_{t - i},w_{t + j}} \right)}} \right)}},{wherein}$ ${{{similarity}\left( {w_{i},w_{j}} \right)} = {\frac{1}{n}{\sum\limits_{k = 1}^{n}{{{{hist}_{i}\lbrack k\rbrack} - {{hist}_{j}\lbrack k\rbrack}}}}}},$ and wherein {dot over (S)}_(w)(t) is the similarity measure time derivative, w_(i), w_(j) are corresponding windows-of-interest in a pair of successive snapshots, [n₁,n₂] is the duration neighborhood about the snapshot incorporating the occlusion over which similarity is being sought, h is a positive increasing function with h(1)=1, and hist is a histogram of spatial intensity values in the window-of-interest; and completing the processing of the snapshot incorporating the verified change event.
 2. The method of claim 1, wherein the sampling interval is from a few seconds to a few minutes.
 3. The method of claim 1, further comprising defining one or more windows-of-interest in each snapshot, and measuring the similarity in each window-of-interest in each snapshot.
 4. The method of claim 3, wherein the similarity measure for a window-of-interest in snapshot is defined as ${{S_{w}^{0}(t)} = {\frac{1}{W}\sqrt{\sum\limits_{x \in W}{\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)}}}},$ where x_(t) represents the pixel intensity for a pixel in a window-of-interest W of snapshot t, and x _(t) is a spatial intensity average in the window for the snapshot.
 5. The method of claim 4, wherein the similarity measure is normalized to the spatial intensity scale of the window-of-interest according to the formula ${S_{w}(t)} = {\frac{1}{{W}*{\overset{\_}{x}}_{t}}{\sqrt{\sum\limits_{x \in W}{\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)*\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)}}.}}$
 6. The method of claim 1, wherein the change in the similarity measure is determined from the time derivative of the similarity measure.
 7. The method of claim 1, wherein h(x)∝x².
 8. The method of claim 1, wherein a false positive includes a change of illumination.
 9. The method of claim 1, wherein the predetermined threshold is based on an analysis of the fluctuations in the similarity change between successive pairs of snapshots.
 10. The method of claim 9, wherein the threshold is more than three standard deviations greater than the mean fluctuation magnitude of the similarity change between successive pairs of snapshots.
 11. A program storage device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform the method steps for detecting events in a video sequence said method comprising the steps of: providing a video sequence; sampling the video sequence at regular intervals to form a series of snapshots of the sequence; measuring a similarity of each snapshot; measuring a similarity change between successive pairs of snapshots, wherein if a similarity change magnitude is greater than a predetermined threshold, a change event has been detected; verifying the change event to exclude a false positive, wherein a false positive includes an occlusion; eliminating an occlusion by weighting a time derivative of the similarity measure according to the definition $\begin{matrix} {{f_{w}(t)} = {{g(t)}*{{\overset{.}{S}}_{w}(t)}}} \\ {wherein} \\ {{{g(t)} = {h\left( {\min\limits_{{i \in {\lbrack{n_{1},n_{2}}\rbrack}},{j \in {\lbrack{n_{1},n_{2}}\rbrack}}}{{similarity}\left( {w_{t - i},w_{t + j}} \right)}} \right)}},} \\ {wherein} \\ {{{{similarity}\;\left( {w_{i},w_{j}} \right)} = {\frac{1}{n}{\sum\limits_{k = 1}^{n}\;{{{{hist}_{i}\lbrack k\rbrack} - {{hist}_{j}\lbrack k\rbrack}}}}}},} \end{matrix}$ and wherein {dot over (S)}_(w)(t) is the similarity measure time derivative, w_(i), w_(j) are corresponding windows-of-interest in a pair of successive snapshots, [n₁,n₂] is the duration neighborhood about the snapshot incorporating the occlusion over which similarity is being sought, h is a positive increasing function with h(1)=1, and hist is a histogram of spatial intensity values in the window-of-interest; and completing the processing of the snapshot incorporating the verified change event.
 12. The computer readable program storage device of claim 11, wherein the sampling interval is from a few seconds to a few minutes.
 13. The computer readable program storage device of claim 11, wherein the method further comprises defining one or more windows-of-interest in each snapshot, and measuring the similarity in each window-of-interest in each snapshot.
 14. The computer readable program storage device of claim 13, wherein the similarity measure for a window-of-interest in a snapshot is defined as ${{S_{w}^{0}(t)} = {\frac{1}{W}\sqrt{\sum\limits_{x \in W}{\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)}}}},$ where x_(t) represents the pixel intensity for a pixel in a window-of-interest W of snapshot t, and x _(t) is a spatial intensity average in the window for the snapshot.
 15. The computer readable program storage device of claim 14, wherein the similarity measure is normalized to the spatial intensity scale of the window-of-interest according to the formula ${S_{w}(t)} = {\frac{1}{{W}*{\overset{\_}{x}}_{t}}{\sqrt{\sum\limits_{x \in W}{\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)*\left( {x_{t} - {\overset{\_}{x}}_{t}} \right)}}.}}$
 16. The computer readable program storage device of claim 11, wherein the change in the similarity measure is determined from the time derivative of the similarity measure.
 17. The computer readable program storage device of claim 11, wherein h(x)∝x².
 18. The computer readable program storage device of claim 11, wherein a false positive includes a change of illumination.
 19. The computer readable program storage device of claim 11, wherein the predetermined threshold is based on an analysis of the fluctuations in the similarity change between successive pairs of snapshots.
 20. The computer readable program storage device of claim 19, wherein the threshold is more than three standard deviations greater than the mean fluctuation magnitude of the similarity change between successive pairs of snapshots. 